File size: 8,541 Bytes
e9a9910 4cda01c e9a9910 dcf2a02 4cda01c dcf2a02 4cda01c dcf2a02 4cda01c dcf2a02 4cda01c ba47ed0 dcf2a02 4cda01c dcf2a02 4cda01c 11b1e07 4cda01c dcf2a02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | ---
license: cc-by-nc-4.0
task_categories:
- robotics
- image-to-3d
language:
- en
tags:
- vitra
- mano
- hand-object-interaction
---
# egotouch-annotations-v1
**Annotations only — no images, no video.**
VITRA-style hand episodes for **EgoTouch**, with per-hand instructions and paraphrases.
| | |
|---|---|
| episodes | **107,364** |
| training samples (`index_frame_pair` rows) | **3,123,675** |
| annotation | MANO pose + world/camera joints + per-frame extrinsics |
| text | one instruction per episode + 1.93 paraphrases on average |
| **images / video** | **not included** — see *Getting the frames* below |
### What we did
**Episodes are ours.** The source release ships either raw video or differently-segmented
clips, so we re-cut it with VITRA's method — speed minima of the 3D wrist in world space:
gaussian smooth (sigma=1.0) -> local speed minima in a fixed window (win=15, i.e. 0.5 s
at 30 Hz) -> merge runs shorter than min_seg=16 -> pad 2 frames on each end
sigma and win are quantities in *time*, converted per source frame rate. Left and right hands
are cut independently, with the other hand's motion ignored.
**Instructions are ours.** Two rounds, both with `Qwen3.5-122B-A10B-FP8`:
round 1 captions 8 frames per episode with the palm's future trajectory drawn on them;
round 2 checks the sentence belongs to that hand, strips same-hand references
("Rinse the right hand." -> "Rinse the hand.", because training already prepends
`Left hand: ... Right hand: ...`), and writes 1-3 paraphrases.
**Episodes with no instruction are not included.** Round 1 returns `N/A` when an episode
shows no object interaction. Those episodes are excluded from both the archive and the
index, so every episode here has a usable instruction.
### Tactile
**This is the only domain here that carries tactile data.** Each episode has an extra
top-level `tactile` dict (not part of VITRA's original format):
```
d["tactile"]["left"] ndarray (T, 21, 21) float32 # NaN outside the sensor area
d["tactile"]["right"] ndarray (T, 21, 21) float32
```
Row `t` of `tactile[hand]` is the pressure map for the **same frame** as row `t` of
`joints_worldspace` and of `video_decode_frame` — i.e. tactile and pose are aligned
frame-for-frame, no resampling.
We verified that mapping end to end rather than assuming it:
- the source release ships one pressure row per video frame (`frame_index` runs
`0..n-1`, strictly increasing, at exactly 30.00 fps) — checked on sampled scenes;
- pressure length equals video frame count for **all 1,566 scenes x 2 hands**, with zero
exceptions, so nothing was silently truncated;
- our stored values reproduce the official `pressure_grids.npz` at every frame; the
per-frame pressure totals cross-correlate at **lag 0 with r = 1.0000**, and a spatial
transpose does *not* match, ruling out both a time shift and an axis swap;
- within an episode, the tactile slice is the same `[a:b]` window as the pose slice
(byte-identical on every sampled episode).
Values are the official ones passed through **float16** at one intermediate stage, so they
differ from the source by at most one float16 half-ULP (max observed 2.41e-4 on a
0-1 normalised map). Positions of `NaN` are preserved exactly.
### Files
```
egotouch.tar -> Annotation/egotouch/episodic_annotations/*.npy
episode_frame_index.npz index_frame_pair (N,2) uint32 + index_to_episode_id (E,)
```
`index_frame_pair` **row number is the sample id**: row r = (episode ordinal, frame within
that episode). `len(index_frame_pair)` is the size of the training set.
```python
import numpy as np
# tar -xf egotouch.tar
z = np.load("episode_frame_index.npz", allow_pickle=True)
ep_slot, frame_id = z["index_frame_pair"][sample_id]
eid = str(z["index_to_episode_id"][ep_slot])
d = np.load(f"Annotation/egotouch/episodic_annotations/{eid}.npy", allow_pickle=True).item()
rgb_frame_id = int(d["video_decode_frame"][frame_id])
```
Each `.npy` is a dict with `video_name`, `video_decode_frame`, `intrinsics`,
per-frame `extrinsics` (world->camera), `anno_type` (which hand this episode is for),
`text`, `text_rephrase`, and a `left`/`right` dict holding `beta`, `hand_pose`,
`global_orient_worldspace`, `transl_worldspace`, `joints_worldspace`, `kept_frames`.
`text[hand] = [(sentence, (0, T))]` and `text_rephrase[hand] = [([paraphrases...], (0, T))]`.
### Getting the frames
`video_decode_frame` indexes the **source** video, which we do not redistribute.
Get it from EgoTouch — https://huggingface.co/datasets/zhouzhoujy/EgoTouch, then decode by index (we use `decord`; a self-maintained
sequential counter drifts silently if the decoder ever skips a frame).
### Known limitations
- Paraphrase count averages 1.93, not a fixed number. Past 3 the model starts inventing;
a sentence with no prepositional phrase honestly supports only one or two.
- Verified: the index lists exactly the episodes that have an instruction, every episode's
stored frame count matches its index rows, and no index entry points at a missing episode.
---
## Revision — 2026-09-08 (re-cut)
**This release replaces the previous one. The previous episodes contained invalid frames
and should not be used.**
A defect in our episode-cutting step let frames with `kept_frames == False`
(invalid hand pose — all-zero or NaN wrist coordinates) stay inside published episodes.
The validity mask was only used to keep a cut point from landing on an invalid frame; it did
not constrain what a segment contained. Worse, a run of invalid frames could suppress cutting
altogether, so the gap was swallowed into one long segment instead of being excluded.
Every episode here is now built from a run of consecutive valid frames, so `kept_frames` is
all-`True` by construction — verified over the whole collection: **568,369 episodes /
16.5 M frames, zero `kept_frames == False`**. Both instruction rounds were regenerated for the
new segmentation.
Segment counts and episode ids therefore changed, and the index and splits were rebuilt:
| | previous | this release |
|---|---:|---:|
| episodes on disk | 147,386 | 143,525 |
| episodes published (with an instruction) | 111,159 | 107,364 |
| training samples | 3,687,389 | 3,123,675 |
Splits are `video_test` (test owns whole videos disjoint from train/val; train and val share
the remaining videos and are split at the episode level), balanced on **frames** at 90/5/5,
seed 1.
<!-- COLLECTION_TABLE_START -->
| dataset | episodes | training samples | our contribution | size | HF |
|---|---:|---:|---|---:|---|
| **EPIC-KITCHENS-100** | 149,570 | 4,019,534 | episodes + text | 8.70 GB | [`epic30-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/epic30-annotations-v1) |
| **EgoTouch** | 107,364 | 3,123,675 | episodes + text + **tactile** | 17.02 GB | [`egotouch-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egotouch-annotations-v1) |
| **GigaHands** | 70,486 | 2,266,087 | episodes + text | 2.89 GB | [`gigahands-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/gigahands-annotations-v1) |
| **Ego-Exo4D** | 67,051 | 1,757,474 | text only | 4.09 GB | [`egoexo4d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egoexo4d-annotations-v1) |
| **OakInk2** | 29,058 | 1,052,924 | episodes + text | 1.56 GB | [`oakink2-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/oakink2-annotations-v1) |
| **TACO** | 23,757 | 736,136 | episodes + text | 1.34 GB | [`taco-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/taco-annotations-v1) |
| **HOT3D** | 18,805 | 619,680 | episodes + text | 1.51 GB | [`hot3d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/hot3d-annotations-v1) |
| **ARCTIC** | 12,610 | 425,796 | episodes + text | 0.85 GB | [`arctic-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/arctic-annotations-v1) |
| **H2O** | 5,845 | 200,332 | episodes + text | 0.40 GB | [`h2o-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/h2o-annotations-v1) |
| **Tachin** | 2,669 | 79,584 | episodes + text + **tactile** | 3.38 GB | [`tachin-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/tachin-annotations-v1) |
| **total** | **487,215** | **14,281,222** | | **41.7 GB** | |
> **Something-Something V2 was dropped** from the collection (12 fps against
> 30 fps everywhere else, so a 16-step action chunk spans 1.33 s instead of 0.53 s).
> The repository still exists but should not be used.
<!-- COLLECTION_TABLE_END -->
|